{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# I/O with Scenes\n",
    "\n",
    "Φ<sub>Flow</sub> comes with a couple of I/O functions to store fields, such as `CenteredGrid` or `StaggeredGrid`.\n",
    "These are stored according to the [scene format specification](https://tum-pbs.github.io/PhiFlow/Scene_Format_Specification.html) which is designed to store sequences."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from phi.flow import *"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Writing Data\n",
    "\n",
    "A scene stores one sequence of data. Batched data can also be stored in a single scene (see below).\n",
    "To create a new scene, we pass the directory and name. An index suffix is automatically appended to the name."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "\u001B[94m./data\\sim_000000\u001B[0m"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene = Scene.create('./data', name='sim')\n",
    "scene"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can list all scenes in the directory."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(\u001B[94m./data\\sim_000000\u001B[0m,)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scene.list('./data')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's write some data to the scene.\n",
    "Here, we sample random values on a grid according to a `smoothness` parameter, which we also want to store."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 2 Axes>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 2 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAFkCAYAAABo/C4gAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAjcElEQVR4nO3de5BkZ33e8eeZ7um57n0kJKTFkohMYhTKkLV8IbFlBM5CKGRTkEJVEBGT2iQVYXBBuIQkclJxQmFCTFWcywYUcEUWprgYVeyykB0ohYoRuiBAYhESIKRFl93VSnubW19++WNayVrs7uz2/LrfOWe+n6qu3e7pfs7bp/uc/vXb73mPI0IAAAAAyhgr3QAAAABgI6MgBwAAAAqiIAcAAAAKoiAHAAAACqIgBwAAAAqiIAcAAAAKGlpBbvtG2wds3/ec299u+wHb99v+0LCWDwAAAFTBMHvIPyFp98k32P5lSddIeklEvFjSh4e4fAAAAGDdG1pBHhG3Szr8nJv/saQPRsRS/z4HhrV8AAAAoAqaI17eT0r6W7Z/W9KipHdHxJ2nuqPtPZL2SNJYs/U3Jrecn9KAsXbemUnHlrtpWenstKho5GX1xvO+A0Zes1Yk5kXiV93MLPfysiTJiZtA5suZev7hzPdF8nu218rLiom8N8fY2Po9A3QkvgjRzts4mwtpUWrM522YXu6kZUmSmo20qO5UXgnTTdyWUndmUuoOLfMzYOHg/kMRcV5eYq6//csz8dThwbaFu7+5dGtE7F79nnlGXZA3JW2T9HOSfkbSp21fFhE/9naLiL2S9krSzNzO+Guv+82UBkw/mbdzmf7hkbQsSdKPr4bBo1qJO6rZibSsE8/Py+pO5O71eolfPDrTaVFqz+S1q3Ust1AaP574BTexuO8l7tm645lfbtOiJEnHX5CX1XnhYlrW1PRSWla25eW8N0f7ibwNfcc38t5nO75xNC1r7JHkH7K3b0mLOvKSHWlZx16Q+EUh72NOkjS2nJc1fiJvn/2N//SuH6aFDcFTh7v62q2D7SQbFz44l9ycVY26IN8v6XP9AvxrtnuS5iQdHHE7AAAAUFMhqafkn4WHaNQF+R9JeoWkL9v+SUktSYdG3AYAAADUWqgbFOSyfbOkqyTN2d4v6QZJN0q6sT8V4rKk6041XAUAAADYKIZWkEfEtaf505uHtUwAAABgZchKdfp8Rz1kBQAAABg6xpADAAAAhYRC3QqNiqYgBwAAQO0wZAUAAAAoJCR1K1SQJ54DEAAAAMC5ooccAAAAtcOQFQAAAKCQkDioEwAAACipOpMeUpADAACgZkJRqYM6KcgBAABQLyF1q1OPM8sKAAAAUBI95AAAAKiVEGPIAQAAgIKsrly6EWetEgW5u9LEkZzvOY2lvO9L0cpdfT6xmJfV6aZlNdKSpEa7lZa1sCN3xFV3Im/DjcSV1t6Ul9Wdyt05zV+Ql9edyhvs123lZUUzb5/RmE9+z164lJb1c5c8nJZ15dYfpGU9vrw1LUuS9h29IC3rW8cuTstqz+TtG8cWO2lZ3YMH07IkqdnI2wamDm5Oy4qxvH3Z/Pm52/lYO29/Nvl0hQZVr1FI6lXo6VaiIAcAAADOBT3kAAAAQCGhahXkzLICAAAAFEQPOQAAAGqnF9XpIacgBwAAQK1UbcgKBTkAAABqJWR1KzQyuzotBQAAAM5SLzzQZTW2d9r+ku19tu+3/Y61tpUecgAAANTKkIesdCS9KyLusb1J0t22b4uIbw8aSA85AAAAcJYi4vGIuKf//2OS9km6aC2Z9JADAACgZqxuDNzvPGf7rpOu742Ivadcin2JpJdKumPQhUkU5AAAAKiZkNQbfCDIoYjYtdqdbM9K+qykd0bE0UEXJlGQAwAAoIaGOe2h7XGtFOM3RcTn1ppHQQ4AAIBaiVjTkJUzsm1JH5e0LyI+kpHJQZ0AAAConZ480OUsvFzSWyS9wva9/ctr1tJWesgBAACAsxQRX5Fyx8NQkAMAAKBWVuYhr85AkKG11PaNtg/Yvu8Uf3u37bA9N6zlAwAAYKNaGUM+yKWEYS71E5J2P/dG2zslvUrSI0NcNgAAADaoZ6c9HORSwtCWGhG3Szp8ij/9B0nv0cq6AgAAANJ1wwNdShjpGHLbr5P0o4j4xsqMMWe87x5JeySpNbU1eeh8jmjkNmqs3UnL6m2eTsvqbJ1My2pP530HzF7/vVZiVuKW1Z3K++7abuZ+D47xvLyxHUtpWROtvG1pYjwvq9PL7QN54fan0rKu3r4vLes1Mw+lZbXTklb8fmPVc32ctQc2nZ+WFWN5O6BeK28H5GZumRBLy2lZY8vdtCxn7hqT65Vo5gV2Ez/n1ruQKzWGfGQFue1pSR+Q9Ctnc//+KUr3StLstp30pgMAAKCWRtlD/kJJl0p6tnf8Ykn32L4yIp4YYTsAAABQc71CB2gOYmQFeUR8S9L/+33P9sOSdkXEoVG1AQAAAPXHtId9tm+W9BeSXmR7v+23DWtZAAAAwLNCgx3QWbuDOiPi2lX+fsmwlg0AAICNrdQUhoPgTJ0AAAColQgVO8nPIKrTUgAAAKCG6CEHAABAzVi99XgSm9OgIAcAAECthKo1ZIWCHAAAALVTpWkPKcgBAABQKyGrV2gKw0FQkAMAAKB2qtRDXp2WAgAAADVEDzkAAABqJST1OKgTAAAAKMXqMu0hAAAAUAY95AAAAEBh9JADAAAAhUSYHvJs0ZCWNuWs1G5rPCVHkrpTuS/02LbJtKzOTCMta/78vKz2dN631V4rLUqSlLndtjdFWlaM5WX1pnppWZI0t/OZtKzLtx1My2qOddOyJhKzto7Pp2VJ0gsmDqdlvXjiR2lZFzZn07KyvXT64bSsP9784rSsE428ddbZMpGW1brsJ9KyJEnjeWVHe1Pe53nm/j976uvIe5rqTOdlIVclCnIAAADgXHTpIQcAAADKCEk9xpADAAAApZgecgAAAKCUlWkP6SEHAAAAiumqOj3k1WkpAAAAUEP0kAMAAKBWQmbICgAAAFBSr0IDQSjIAQAAUCsRUpcecgAAAKAchqwAAAAAhayMIa/OkJXqtBQAAACoIXrIAQAAUDtdMWQFAAAAKIIzdQIAAABFMYZckmT7RtsHbN930m2/Y/s7tr9p+/O2tw5r+QAAANi4evJAlxKG+dXhE5J2P+e22yRdEREvkfRdSe8f4vIBAACwAT07D/kglxKGVpBHxO2SDj/nti9GRKd/9auSLh7W8gEAAIAqKDmG/Ncl/eHp/mh7j6Q9ktSa2aasYUCL2/K++SxsH0/LSpf4BW95S15WdyIvK1tjKTEs8atu6hC4Vi8xTHrZ+Y+mZb1627fSsi5pPpWWdaA7m5a1GLn7jIuaz6TmZfnaUt77rKFIy5Kkg53z07IisSetvSktSvMXtNKy3N2cliVJi3N5bVvYnrdzXNqR91p2856iJGmsnZfVPJGXVQVVGkNepCC3/QFJHUk3ne4+EbFX0l5JmpnbmbtHBgAAQG2tnBiIWVZOy/Z1kl4r6eqIoNAGAABAulIHaA5ipAW57d2S3ivplyJifpTLBgAAwMbAPOR9tm+WdJWkOdv7Jd2glVlVJiTdZluSvhoR/2hYbQAAAMDGxBhySRFx7Slu/viwlgcAAABUEWfqBAAAQL0EB3UCAAAAxYQ4qBMAAAAoih5yAAAAoBBmWQEAAAAKq1JBXp35YAAAAIAaooccAAAAtRJilhUAAACgKGZZAQAAAEqJao0hpyAHAABArTDLCgAAAFBYlQpyZlkBAAAAzoHtG20fsH1fRh4FOQAAAGrl2VlWBrmcpU9I2p3V3koMWelOSMcuyfnZIcYiJaeflpglNefX508rqb/4OG+d9cbToiRJ3cm8rF4r972RJvX9Ly338nYhm8YW0rKe12inZY37aFrW99o70rIk6Ynu5rSs+xZ2pmU9urg9LWuhm7uhP3jkvLSsJx/Oe56Jux8tbcnbaY+fyF3/7Zm8ts1fmJe1cOlyWlZzqpOWJUndTl7f6dJjE2lZVRBDHLISEbfbviQrrxIFOQAAAHAu1jDt4Zztu066vjci9iY06bQoyAEAAFArsbZpDw9FxK7M9qyGghwAAAC1M8whK9k4qBMAAAAoiIIcAAAANTPcWVZs3yzpLyS9yPZ+229bS2sZsgIAAIDaGfIsK9dm5lGQAwAAoFZC1TpTJwU5AAAA6iVWZlqpCgpyAAAA1M4a5iEfOQ7qBAAAAAqihxwAAAC1EqrWPOQU5AAAAKiZs5/CcD2gIAcAAEDtcFAnAAAAUBBDVgAAAIBCIqpVkA9tlhXbN9o+YPu+k27bbvs22w/2/902rOUDAAAAVTDMaQ8/IWn3c257n6Q/j4jLJf15/zoAAACQqhce6FLC0AryiLhd0uHn3HyNpE/2//9JSb86rOUDAABg44oY7FLCqMeQPy8iHpekiHjc9vkjXj4AAAA2gCqNIV+3B3Xa3iNpjyQ157Yo/urxlNxms5uSI0mL8620LElqH8nLG1vMexNG4u8o0Ur86pm8ncV4Ly2rMdtOy2q1OmlZM5PLaVmSdMnUU6l5We5cyvuu/1g771CXx9tb07Ik6XhnIi3rweN56yzzJ9/Hj21Ky5Kkw49vScua3p/3ETp+LC1KmTXI/HmNvDBJC+fnNW5pR94+uzWbt2/cvvlEWpYkTY/nfZ4c2DSblrXehVypgnyYY8hP5UnbF0pS/98Dp7tjROyNiF0RsauxeWZkDQQAAED1xYCXEkZdkN8i6br+/6+T9IURLx8AAABYV4Y2ZMX2zZKukjRne7+kGyR9UNKnbb9N0iOS3jis5QMAAGCDqtg85EMryCPi2tP86ephLRMAAACQVG78yQDW7UGdAAAAwKDoIQcAAAAKKjWn+CAoyAEAAFAroWr1kI96lhUAAAAAJ6GHHAAAAPUSyj1L1pBRkAMAAKB2GEMOAAAAlERBDgAAAJTiSh3USUEOAACA+qlQDzmzrAAAAAAF0UMOAACAeolqzUNOQQ4AAID6qdCQFQpyAAAA1BA95AAAAEA59JDnmhpv668//7GUrOPtiZQcSXpqYiYtS5IWZpbTsjqdvON1m81eWla3m9eu7An/pybaaVkXbTmSlvW8yWNpWVONvOcoSXPjeW27a/6ytKwfLu5Iy/re0bm0rOVeIy1Lkg6fmE7LWlwaT8vqdvKeZ++pVlqWJE0+lde2qYN5OyF30qK0uCOvV/DEttwdbXtLNy0rZvJW2vRk3ufvedMn0rIkqTWW9zyfN5W3z/52WtIQVaggZ5YVAAAAoKBK9JADAAAAZy0kMcsKAAAAUE720NZhoiAHAABA/VCQAwAAAAUxZAUAAAAoxxXqIWeWFQAAAKAgesgBAABQLyHGkAMAAADlmDHkAAAAQFH0kAMAAAAFUZADAAAABVWoIGeWFQAAAKAgesgBAABQLyEO6gQAAABK4sRAq7D9m7bvt32f7ZttT5ZoBwAAAGoqBrwUMPKC3PZFkn5D0q6IuEJSQ9KbRt0OAAAAYD1YtSC3fb3tbcnLbUqast2UNC3pseR8AAAAbGCOwS4lnM0Y8gsk3Wn7Hkk3Sro1IgZubkT8yPaHJT0iaUHSFyPii8+9n+09kvZI0uwFMzp/8vigi/xLepkD/KfzoiSpPdlIyzp0fCYtK1Onk/ejTKvVScuSpC1Ti2lZnV7e8zzWmUjLeuRE7nfrh47NpWU9NZ/3np1fGk/LyjR/NHd0XvNAKy0r80No4kjefnbycO6nY2MpL691vJeWtbglb5+xvCXvObZ3LqVlSdLEVDstq9HIW/9O3AAOJu7LJGnH1HxaVlYthXyr7gEi4p9LulzSxyW9VdKDtv+t7RcOssB+b/s1ki6V9HxJM7bffIrl7o2IXRGxa3JbXkECAACADSA82KWAs/pK3u8Rf6J/6UjaJukztj80wDJfKekHEXEwItqSPifpFwbIAQAAAH7coAd0rtchK7Z/Q9J1kg5J+pikfxoRbdtjkh6U9J5zXOYjkn7O9rRWhqxcLemuc8wAAAAATq9C0x6ezRjyOUmvj4gfnnxjRPRsv/ZcFxgRd9j+jKR7tNLb/nVJe881BwAAADidKs1DvmpBHhH/8gx/2zfIQiPiBkk3DPJYAAAAYFUVKsiLnBgIAAAAwIqzGbICAAAAVAs95AAAAEAZg54U6GzHndvebfsB2w/Zft9a20tBDgAAgPoZ0jzkthuSfk/SqyX9lKRrbf/UWppKQQ4AAID6Gd485FdKeigivh8Ry5I+pZWTXg6MMeQAAAConTVMezhn++Rz5OyNiJOn6L5I0qMnXd8v6WcHXpooyAEAAICTHYqIXWf4+6nGtazpEFIKcgAAANTP8GZZ2S9p50nXL5b02FoCGUMOAACAehnuLCt3Srrc9qW2W5LeJOmWtTSXHnIAAADUz5B6yCOiY/t6SbdKaki6MSLuX0smBTkAAADqZ4gnBoqIP5H0J1l5FOQAAAConTXMsjJyjCEHAAAACqpED/lSt6nvHZ1LyeqdcqaawWwaX0zLkqTJRictayIx69D8TFrWcjvvLdfpNNKyJGmxk9e2pW5e2x45vC0ta+HwVFqWJHkp7zt983he1thy3nbem8jrYpk6kdcuSRo/mpc11s7Lmnqql5Y1+VRiwyRFM+991plO7NNKfGv0JvKyNm1eyAuTNDd7Ii0r83Ou3cvbZx84NpuWJUnHF/Ne0IXOeFoWclWiIAcAAADOSYWGrFCQAwAAoF7OfgrDdYGCHAAAAPVDQQ4AAAAUVKGCnFlWAAAAgILoIQcAAECtWIwhBwAAAMqiIAcAAAAKYZYVAAAAoDAKcgAAAKCgChXkzLICAAAAFEQPOQAAAGqHMeQAAABASRTkAAAAQCEhCnIAAACgpCoNWSlyUKftrbY/Y/s7tvfZ/vkS7QAAAEBNxYCXAkr1kH9U0p9GxBtstyRNF2oHAAAAUNTIC3LbmyX9oqS3SlJELEtaHnU7AAAAUF8MWTmzyyQdlPTfbX/d9sdszxRoBwAAAOqKISurLvNlkt4eEXfY/qik90n6FyffyfYeSXskqXX+Zh1vt1IW3u42UnIkaamTu/ou2fxUWtaFU0fTspa6ec/z2PxkWlank/t98siJqbSs5aW8deYnJ9Kytjycu84aS3l7rsmne2lZzovS0ua8dRaN3D39+Im8vLFuWpSaC3kvQHO+k5YlSdHIez3bszmfS5LUnXBalnt574vFpfG0LEnSbF7UdDPvx/XFbt7zbDUTNyZJC8t5bVtMrlvWtYrNslKih3y/pP0RcUf/+me0UqD/JRGxNyJ2RcSu8S15hRIAAADqzWu4lDDygjwinpD0qO0X9W+6WtK3R90OAAAA1BhDVlb1dkk39WdY+b6kv1+oHQAAAEBRRQryiLhX0q4SywYAAED9VWmWlQ00uh8AAAAbBgU5AAAAUBAFOQAAAFBIMGQFAAAAKKtCBXmJecgBAAAA9NFDDgAAgNphyAoAAABQEgU5AAAAUA495AAAAEApIXrIAQAAgKIqVJAzywoAAABQED3kAAAAqBWLMeQAAABAWRTkAAAAQDmO6lTklSjIO92GDh2ZTcly4u8X881eWpYkzbaW0rI2txbTsg4cy1n3krR4vJWWpaVGXpakdmJW82he27Z+Jy1KW36Q976QpPGn8/K8sJyWpV7ettmZ25SXtWk8LUuSuhN5hwG5m/jB5byo9ubEfYakSNxtOPEjYKydt/6bx/NegIWnJ9OyJGlpS17ZMTndSctqjuW9mEtTuaVVZt2ysJy7D1rXmGUFAAAAKKtKY8iZZQUAAAAoiB5yAAAA1E+FesgpyAEAAFA7VRqyQkEOAACA+qEgBwAAAAoJesgBAACAsipUkDPLCgAAAFAQPeQAAACoFYshKwAAAEBZUZ2KnIIcAAAAtUMPOQAAAFBKqFIHdVKQAwAAoHbcK92Cs8csKwAAAEBB9JADAACgfio0ZKVYD7nthu2v2/6fpdoAAACAenIMdimhZA/5OyTtk7S5YBsAAABQN6FKTXtYpIfc9sWS/o6kj5VYPgAAAOqtVA+57Tfavt92z/aus3lMqR7y35X0HkmbTncH23sk7ZGkxvatWn56MmfJib9FLDktSpL03YVWWtZYo5uW1X1iOi1r4kjed8CxTlqUJKk3nvfeaJ7Ie3PMPNFOy2o9cSwtS5L89NG8sKmkbVxK7RVpHsx7jo35xOcoqb1tKi2rM91Iy3Le7kfuJfdwJcY1Im8Kh8ln8vYZkfdSanlrbr9d5qu5eXwxLasbeet/uZv4Akiab4+nZR1L3gete+U6yO+T9HpJ//VsHzDyHnLbr5V0ICLuPtP9ImJvROyKiF2N2ZkRtQ4AAAAYXETsi4gHzuUxJXrIXy7pdbZfI2lS0mbb/yMi3lygLQAAAKgZa02DIuZs33XS9b0RsXfNjTqDkRfkEfF+Se+XJNtXSXo3xTgAAADSRKxl+OKhiDjj2G/bfybpglP86QMR8YVzXSDzkAMAAKB2hjmFYUS8MjOvaEEeEV+W9OWSbQAAAEANVWfWw3InBgIAAACGpeC0h79me7+kn5f0x7ZvXe0xDFkBAAAAkkTE5yV9/lweQ0EOAACAeglJ2ecxGCIKcgAAANRPdepxCnIAAADUzzBnWclGQQ4AAID6GXwe8pGjIAcAAEDtVKmHnGkPAQAAgILoIQcAAEC9hDioEwAAACjFkswYcgAAAKCgXukGnD0KcgAAANQOPeQAAABAKYwhH4KVgUApUeOH1+9T7h3Ja1vjuNOypp5Ji9JYJy+rl/xSupe3ziaeydsLtI6007K8sJSWJUmaaKVFRSNv0qfM5+l24pu20cjLkjS2nLj+Z/M2qEh8mk7+ybnbynufZe6DeonrrLmQl9U6kjsZ28GnN6VlPdA8Py1rSytvpR1amE3LkqTFdmJt0KjQGI4NZv1WpwAAAMBAghMDAQAAACVV6cRAFOQAAACoH3rIAQAAgEIi/xiUYaIgBwAAQP1UqIc89/BpAAAAAOeEHnIAAADUT3U6yCnIAQAAUD+cqRMAAAAoiYIcAAAAKCQkMcsKAAAAUIYVlRqywiwrAAAAQEH0kAMAAKB+KtRDTkEOAACA+qEgBwAAAAqp2EGdIx9Dbnun7S/Z3mf7ftvvGHUbAAAAUG+OGOhSQoke8o6kd0XEPbY3Sbrb9m0R8e0CbQEAAEAdVWjIysh7yCPi8Yi4p///Y5L2Sbpo1O0AAAAA1oOiY8htXyLppZLuOMXf9kjaI0mN7VtH2i4AAABUWVSqh7xYQW57VtJnJb0zIo4+9+8RsVfSXkmavHhnjD+d09SpA07JkaTmfO4LHYm/V2S2rbmYl9Wezlv/metLkpx48Mf4Ql5YjCWus4lWWpYkqZf3PN3upGVltktjeW+0mBxPy5KkpR0TaVnHLmqkZWVuS2PtvCxJas/mbU9KjGodydvPNpbysiYPpUVJkhbvn0nL+v6ledvT7Pb5tKzmWO6RhO1u3rZ54pmptKx1L0RBvhrb41opxm+KiM+VaAMAAABqrEKzrIy8ILdtSR+XtC8iPjLq5QMAAKD+Ss2YMoiRH9Qp6eWS3iLpFbbv7V9eU6AdAAAAqKuIwS4FjLyHPCK+otSRdwAAAEB1caZOAAAA1EtI6lVnyAoFOQAAAGqGaQ8BAACAsijIAQAAgIIoyAEAAIBCKjaGvMS0hwAAAAD66CEHAABAzYQU1TlVJwU5AAAA6ocx5AAAAEAhFRtDTkEOAACA+qGHHAAAACioQgU5s6wAAAAABdFDDgAAgJqJSvWQU5ADAACgXkJSj2kPU7krjR9xStb0E3kvzuyji2lZkqRGznOUpF4zbzRSt5U5sqmRluTkL77t6bz1v7gtb511JibSsmbGc0epNY8vp2W5m7dtupOXFYnb0vK2ybQsSTr6grxd+LFL8jYoJ34GNhbztktJimbijiPxeTYX8rKmD3XTsiYP5+5oZx/L256eXmqlZR2/LHHfmPuWlZfy2jZ+Irlx6x095AAAAEBBFOQAAABAKVGpeciZZQUAAAAoiB5yAAAA1EtIERzUCQAAAJRToSErFOQAAACoHw7qBAAAAAqJqNQ85BzUCQAAgPqJGOyyRrZ/x/Z3bH/T9udtb13tMRTkAAAAQJ7bJF0RES+R9F1J71/tAQxZAQAAQO1EoSErEfHFk65+VdIbVnsMBTkAAABqJmf4SYJfl/SHq92JghwAAAD1ElrLtIdztu866freiNh78h1s/5mkC07x2A9ExBf69/mApI6km1ZbIAU5AAAA6mfwEwMdiohdZ4yOeOWZ/m77OkmvlXR1xOpd9RTkAAAAqJWQFIVODGR7t6T3SvqliJg/m8cwywoAAACQ5z9K2iTpNtv32v4vqz2gSA95/5vDRyU1JH0sIj5Yoh0AAACooYi1DFlZ46Ljr5zrY0ZekNtuSPo9Sa+StF/SnbZviYhvj7otAAAAqKdSQ1YGUaKH/EpJD0XE9yXJ9qckXSOJghwAAAA5CvWQD8JnceBn7gLtN0jaHRH/oH/9LZJ+NiKuf8799kja0796haT7RtpQnGxO0qHSjdjAWP9lsf7LYv2Xxfovaz2v/5+IiPNKN+J0bP+pVtbfIA5FxO7M9qymRA+5T3Hbj30r6M/3uFeSbN+12vQzGB7Wf1ms/7JY/2Wx/sti/ZfF+h/cqAvqtSoxy8p+STtPun6xpMcKtAMAAAAorkRBfqeky21farsl6U2SbinQDgAAAKC4kQ9ZiYiO7esl3aqVaQ9vjIj7V3nY3lX+juFi/ZfF+i+L9V8W678s1n9ZrP8NYuQHdQIAAAD4/zhTJwAAAFAQBTkAAABQ0LouyG3vtv2A7Ydsv690ezYi2w/b/pbte23fVbo9dWf7RtsHbN930m3bbd9m+8H+v9tKtrHOTrP+f8v2j/rbwL22X1OyjXVme6ftL9neZ/t+2+/o3842MAJnWP9sAyNge9L212x/o7/+/1X/dt7/G8C6HUNuuyHpu5JepZWpEu+UdG1EcEbPEbL9sKRdEbFeT0xQK7Z/UdJxSb8fEVf0b/uQpMMR8cH+F9NtEfHeku2sq9Os/9+SdDwiPlyybRuB7QslXRgR99jeJOluSb8q6a1iGxi6M6z/vyu2gaGzbUkzEXHc9rikr0h6h6TXi/d/7a3nHvIrJT0UEd+PiGVJn5J0TeE2AUMVEbdLOvycm6+R9Mn+/z+plQ9IDMFp1j9GJCIej4h7+v8/JmmfpIvENjASZ1j/GIFYcbx/dbx/CfH+3xDWc0F+kaRHT7q+X+wYSghJX7R9t+09pRuzQT0vIh6XVj4wJZ1fuD0b0fW2v9kf0sLPxSNg+xJJL5V0h9gGRu45619iGxgJ2w3b90o6IOm2iOD9v0Gs54Lcp7htfY6vqbeXR8TLJL1a0j/p/6QPbCT/WdILJf20pMcl/fuirdkAbM9K+qykd0bE0dLt2WhOsf7ZBkYkIroR8dNaOYv5lbavKNwkjMh6Lsj3S9p50vWLJT1WqC0bVkQ81v/3gKTPa2UoEUbryf7YzmfHeB4o3J4NJSKe7H9I9iT9N7ENDFV/7OxnJd0UEZ/r38w2MCKnWv9sA6MXEc9I+rKk3eL9vyGs54L8TkmX277UdkvSmyTdUrhNG4rtmf6BPbI9I+lXJN135kdhCG6RdF3//9dJ+kLBtmw4z34Q9v2a2AaGpn9Q28cl7YuIj5z0J7aBETjd+mcbGA3b59ne2v//lKRXSvqOeP9vCOt2lhVJ6k+t9LuSGpJujIjfLtuijcX2ZVrpFZekpqQ/4DUYLts3S7pK0pykJyXdIOmPJH1a0gskPSLpjRHBgYdDcJr1f5VWfqoPSQ9L+ofPjudELtt/U9L/lvQtSb3+zf9MK+OY2QaG7Azr/1qxDQyd7Zdo5aDNhlY6TD8dEf/a9g7x/q+9dV2QAwAAAHW3noesAAAAALVHQQ4AAAAUREEOAAAAFERBDgAAABREQQ4AAAAUREEOAAAAFERBDgAAABREQQ4AQ2b7Z2x/0/Zk/wy499u+onS7AADrAycGAoARsP1vJE1KmpK0PyL+XeEmAQDWCQpyABgB2y1Jd0palPQLEdEt3CQAwDrBkBUAGI3tkmYlbdJKTzkAAJLoIQeAkbB9i6RPSbpU0oURcX3hJgEA1olm6QYAQN3Z/nuSOhHxB7Ybkv6P7VdExP8q3TYAQHn0kAMAAAAFMYYcAAAAKIiCHAAAACiIghwAAAAoiIIcAAAAKIiCHAAAACiIghwAAAAoiIIcAAAAKOj/AoSVpY7Q7oB2AAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smoothness = math.random_uniform()\n",
    "data = CenteredGrid(Noise(smoothness=smoothness + 0.5), x=32, y=16)\n",
    "plot(data)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Fields can be stored using the `write` method, which creates a new file with the given name and frame index, in this case `random_noise_data_000000.npz`.\n",
    "\n",
    "Additional non-field values, like the smoothness, can be added as properties. Any JSON-serializable object can be stored this way."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "scene.write(noise_data=data, frame=0)\n",
    "scene.put_properties(smoothness=smoothness)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Reading Data\n",
    "\n",
    "Next, let's read the data back from disk. We can reference our scene using `Scene.list` or `Scene.at`."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "scene = Scene.at('./data', 0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The properties of the scene are stored as a `dict` in `scene.properties`."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "{'smoothness': 0.7946394681930542}"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene.properties"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Fields can be read using [`Scene.read`](https://tum-pbs.github.io/PhiFlow/phi/field/#phi.field.Scene.read) given their name and frame index."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 2 Axes>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(scene.read('noise_data', frame=0))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Batched Data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Scenes can also deal with batched data.\n",
    "When batched data is written to a single scene, only one file is created for each stored field."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 4 Axes>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 4 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "batched_smoothness = math.random_uniform(batch(batch=4))\n",
    "batched_data = CenteredGrid(Noise(smoothness=batched_smoothness + 0.5), x=32, y=16)\n",
    "plot(batched_data, show_color_bar=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "scene.write(batched_noise_data=batched_data, frame=0)\n",
    "scene.put_properties(smoothness=batched_smoothness)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 4 Axes>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 4 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAACBCAYAAADdRhvSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAArfklEQVR4nO2dW4xl2X3Wv/8+91vduvo2PZPxxHasmAgbNJjIASmEJJqYRJPwFL/EQkbDAxYgIpAlIBCFBISEeSFCDNgaPxBbERDFDwiwDMhSgHgGeyYaZ+y5+dYzfavqrsupc917Lx6qHHWd/7c8p7p31bnU95Na3bV6n73X7Vv/vers/7cthAAhhBBCCCGEEA9PMusKCCGEEEIIIcSyoA2WEEIIIYQQQhSENlhCCCGEEEIIURDaYAkhhBBCCCFEQWiDJYQQQgghhBAFUZ51BaahvNIMlUtrx8ry3PjBqd8zWk6OI+aJocodFZOSP0EI/vohUicb+fJk5I8rjad3dMxL/pw5Gc1QIWWRrpuaEq9nuZK5skrJlyXmPx8zs8zJ7wAy0s/jcWQqs/OSXysYqVMMduzgjRtbIYSLU59kxpRXmqF6ee1YWUxTISPlrIyRRPqVfZwdGqlTkk5XZn76RQlkXlBNlab77GGl6JV8UUxTZO0pJb6MaSpGThaALGc6441iWjUyTA+rqf7rNxdKUwCPVSxWAEBgsWrK+RrKvG+NzCO6tsZi1diXl0isstSfNDbcpxKrpo1hMV2Vp4tVRhclfnGmq5QsDGlKFhCAr3+kU5nWYp2fTBw7urWDdK/3sHcAZ8qJYlVKytmxrLsimqJrOLunjGmKxKXSeLrjYrAY9NCxatp5FZm+ZaKfMolVJ4kLNFZRTT3cd0VG7lOiS88JYtVCbLAql9bwnk/99WNlvV6NHptt+fJy13c+uxkbPkpmPYDmat+VjUe+68YD3p3Vt6qurPMtf1zrNomukbk46vg29S75sv5lf4KcBDIACJFg5D7f5ncBF6/tuLJH2nuurF72/cyEBAC91PfdvUHDlb19Y51+nm0ErOpFX2v4OsU2fbWqnzwvP/0b3+FHzyfVy2t476c+fqzsoO/7GgDGu15TyYFfZY30dRaZKyBjgLGfvyWiXQCobfvy5i0/YLVdFgk5o7Y/5+CCb9Nww18na/DJktfIDS8J5KUOX3s21rq+rNFzZfWSn5N5JET0U78A7A7qrmxn3+sMAHKy8WIbvArRCdswHpb7efLiz//WQmkK4LGqH9FVuu37nMUqtukaXebzpdL2u6GM3NDnPR6r6m/7udH+rh/bxjbZjESkNm2sGlwkuiL6Aaa/cQwdftd68fKuK7vS3ndldXIn/LCx6tadVfp59ouspOI7tVrzbYrdtNYqx4997e9+mh43z9BYFbn/Y7GqtM9ilf9sus7nijX8wYHEKiMxEQDqt3x564Yfr/q9h4xVm37+DC6cIFZVpotV1ub9tHnB62ezeeDKuKZ4nKexaujXze2dNv18zjRF7nNZrCpFYhX7RcxLv/CbNFad2iOCZvYZM7ttZi/fV/ZPzOwtM3vx6M9HTuv6Qiwj0pUQxSJNCVEs0pQQp5uD9RyAp0j5vwohfPDoz385xesLsYw8B+lKiCJ5DtKUEEXyHKQpcc45tQ1WCOHLAO6e1vmFOI9IV0IUizQlRLFIU0LMJgfrE2b2KwBeAPCrIYR77/SBxIJ7lnhU4VVPyTPXoeefjR6t++crVzb886IAsNoYuLLu0J9zL/CchdE1/1z8vZZ/trR317epzKuEEXmMe/iov471/fO/gTzXDQAJOfYEOb7Y7/lnY79Dnv/fbPtGdSq+jwFgjzxv2x/5vqvU+XPB7Hn59RWfw9Kqkr6LPNfeH0eS2GbLiXSVWI5m7Xibs0huAc3hYAm9JFG0HMktYs88p2N/nXGN67zX9MeOVn1ZuTfd8/cAkLZ9/cfsuXxm3BFLkGZSO0GKeY+sM8w0gc3fGsnLAoBx5vuEGceUy3ydyHOS60bGc3J+HdaJd34sr2XGnDxWJQGN6vE5n2b895gpyWXIB35tyTu+v1sbPi8YANr1oSvrkfWyZ35dBYDBo0QDHa/Bgx1fVuJLOEYr/pw0h4wZFMTMPEYs4Ypfn9Ht+1yd62Sc1pu+nzsV38cA0Bt7rQ6J+VKZaAUAdfRot3ynNqu+75iZAAAMJ7R+EoOBU+IB7v98rEojBjwshuTEuCWQfKPqGh/XWs33NzPVGlb4fUGfmKeNSayqsFyxyFRhsWp0YUqXjMi6zgzios41hIOBn/8srqyQNapB8vIBYJgRrwOiU2awBnCDnToZz5PEqtjcY5y1Tfu/AfBuAB8EcAPAv4wdaGbPmNkLZvZCuudviIUQf8JUurpfU+NdfoMmhADwoLFqV7FKiAgPpCnFKrGonOkGK4RwK4SQhRByAP8OwId+wLHPhhCeDCE8WV5pnl0lhVgwptXV/ZqqrPJvW4UQDxGrVhWrhGA8qKYUq8SicqYbLDO7et+PvwTg5dixQojpkK6EKBZpSohikabEeePUcrDM7HMAfhLAppldB/CPAfykmX0Qh09LfxvA3zit6wuxjEhXQhSLNCVEsUhTQpziBiuE8FFSvHhvuRNijpCuhCgWaUqIYpGmhJiNi+CJKSU51uvHEx3HxNkM4O6COXFxCVXvpBJze9ru+ufqjRge1evcCWVEnFjGa/64fmt6Z6TO5a4re8/ajiu7sd9xZftd/kxzShzbArm+kTdhA8BoQJx1bvu+22n4t24/8gh3dM2IY8seqX8ScbsJxMVm69aKK+ut+UTaK6v+zeQAd2xbNBILaFcmXARjzkx1r7U+e5M9cdcrlbkTD3Ozqta8C1LMHSht+OtnHV/PIdG0Rd7Q3u54x65HiIvYpDMXwN3+AGA48ppgrowxr6ZB35+3t+8d0HaIM2inzZPDWd/3Bv6cWcad/XLS/pS4ajFnwArpTwColad0wJpzSpZjtXZ8Ho1isarq53ZWJ2swiVXUxRPAPnHHo7GqwdewUeKvnxInv/4qOWnECbJz0ceqJ1b2XNl2r+XK9nu+PQAwHpJ+In2SMMdPAMO+d3zrb7FY5et0+dIuPSdz9+wecLdGRkbWqnvbPlYOV/w6dWnF9zEAtCdiVckiDnJzDItVzAkVAEbkHmZUI8eS9TLmsMjGpURiSKPFXQjHzDG3SXS2OV1MBYD2mjfTeZS4Mx+MfPzokrUeAIbMwZRoiq0nQCRW7fh7td26X/fWVyN22YQDssbF1kMWa/fJ2pG1fd/XSH8CQKPC7/MZZ+0iKIQQQgghhBBLizZYQgghhBBCCFEQ2mAJIYQQQgghREFogyWEEEIIIYQQBbEQJhchGIbZ8arWqzzRLFn1yYf3WKLenk/o65EkVwAAMXVYI4m75Ujy/Igk1VWb0yXKrXd8MiMAXGr567+vc8uVtSs+8fJmzZs8AMBW1yf0suTB/l1uksGSjKv3iBnBgBhPtHwyLwC0mz6hl5mJHNzjdapd94mXFZK7eHDNj9FbEdOTVoMnsy4SAYY0HG9fPWI0kNV9Qnya+r5JyTwfE5MHAMiJ+UW76fu1GjHJGJOxqRD9sTZt1Hny6pWGNzXZIJNlZ+zXieu9NXrOmwfeZGav5xPf+8S4AgAw9Em6SY9oquLXqB0yRgBQJknXOTE4yXp87JKuLzcyTN11X/dYIvlKffE1BfBY1YjEKuuQMSNraOgSQ4Ydbp5gxJBidd3P4VisYgn9lapfg5l5xHrEwORKyxtaPNHadmVv1ddc2a2G1w8A3N738WJEjFYGuxFdEW1U7vn5mvX9cdtVHqsaxDiEmfT0d/nYVW/6cS71Sfy96tt5M2JQ1JmInzEjo3nmRLGK3C/skTmd9okZBjE+AYCEGQi1/HUqkVg1LPlrlaaMdRsNfv93teE1dbnmy+6MvH5u9Pn9HzNEY+ZHg72IpkbEDKTrNZUT05G7EZenGrnXY/ek4y43mSrdI5oiU+dgg5umMDoNP/YxFk9tQgghhBBCCDGnaIMlhBBCCCGEEAWhDZYQQgghhBBCFIQ2WEIIIYQQQghREAthcpEFw/7weBJbpxZ5Ez15czNLrGaJ4ilJOgaApMUTKicZkCRbAOi0ffLv1Y5PqP/RlZuurF3iyd8faH7XlVXM13O76RNyv1p/Fz3nC/kPubKb26uuLCGJiwBQu0uSHEn109QnKQ63uElFd5N8fuSvX7nDE1Qbd3xZMvbjnFV83UcXeTvTyFvkF4kQgEF6fL6WIgYEVD8kyT1QUwWeTD/tyhMzRbjY8on7j7V2XNl7mrdd2dXKPXrOa6S8QtwbbqZeEy+WH6fnPEh98u3dXW8mY/u8Q6ok8b5EcmxzMv3HA/77s3GbXIt0czmi8/IBMWIgl8rLxHSgyXVaiySILxp5MPTGx9sYS8hPfDfCSFlCxjHnPglIGiQpPPiTphEDn7W2T6q/1t51ZT/S9rpaZ+5BAN5Xu+HK6ubreafpk++/2uW6+n+Zj1XX76y7smSP66pGzJdorGqQWFXind/dIOM09mXlSKyqbftrJWTq5FV/zjExlAGA8cT9UACZYHMO01Ql4XGFa4osbmRcIj4LMGJycRI2214X10is+pGW19Rm2d8nAsAPV/2x1Slj1deqXFP91M/LvX1v6JREY9V0msqqfq6OUm6c0V8h8zrzg1zeidRpl0wIMtCBGJGMVvg5R5Xp7//0DZYQQgghhBBCFIQ2WEIIIYQQQghRENpgCSGEEEIIIURBaIMlhBBCCCGEEAWhDZYQQgghhBBCFMRCuAiGYM65bbfPnXw6dW9b0ifOSoOGd/cy4k4CAHnfd9Ne8K53VuI+NO++tu3KfuLC667sI+2XXdlqwt21Hi17d8Bx8MduZVvkOD7sL5WuubL8nu+n+jbfl3e+59tf7nsHnsG6/3xtmzuz9K94x7WEVJ85mwFASqbJ+KI/dtzx9SxFnYpifkOLQx4M/RF3s5ok5uQ3DYE4PgIA81VjQxi79saKdzv7U+23XNmHm6+5sscizpwbJe9kxDT1ZuJt/N4eewczgDu4jfeZpng/NW759jOzNuYimNa5JkarXkDs83YC86ysxmwIidNkzteOIXF/XUQCzDm3Tf78fepVH5cqVa+MQZUMTmRssr6/Vjf4RbBc4XHliXUfqz68/qYr++n2H7uyi8zyDsDVKWPVjcy7eB7k3F3sq3cfc2XZru+nBnG2BYD2dRarfNmo4zVUu8vHc7Dp7wnyCrtO5D6DDPNozX9+vOLLYrFqcv0JCxi6QjAMJmLVMBIXSsTdNimRviHHMWdBAEgToil4TdUb3Nn6Xat3XdmfXfEu0H+x+aore6TEz7lZ8nONaeo6cSG8k3q3TgB4Ad6ZM93zsaoRuf9r3phOUywuxe4ph2v++rkvQuKX0kPIMKct4iLd8HMktjk6iYu0vsESQgghhBBCiII4tQ2WmX3GzG6b2cv3lW2Y2RfN7LWjv/mvfYUQFOlKiGKRpoQoFmlKiNP9Bus5AE9NlH0SwJdCCO8F8KWjn4UQ0/McpCshiuQ5SFNCFMlzkKbEOefUNlghhC8DmHz49GkAnz3692cB/OJpXV+IZUS6EqJYpCkhikWaEuLsTS4uhxBuAEAI4YaZXYodaGbPAHgGAMoXV5FNJEf3I8nz3QOffBhIonmo+6S26k3eHaHk96FZ018/6/Ak35sHHVdW3/THjgPb7/Jk5G7uE+0HJMnxtdQnGL86uELP+fbdVVdWu+PbufotnlDbvOkzDZORr1Pzth+PrMbHc3TDlx9c8mUjX/WjcpbQSJLvN30yabvJzRCG47nzhplKV8c0tbmKg/7xBPKQR5KviTFBnpJjSZJw0ue/wwkkcXjU8sceRJKZ98bc5GYaSsbbyZKE93Ov0zuZn2xvDfnTLne63qSlvOPnDzOzAID2TV+nyj5ZE8jH8yrv+1HHlw/XfBlL8AeAtEkuTy4Vyn6diOXYDwbTGa6cIQ8cq0aTJhfj6WNVzjRIzJMqdyOxykhcavuycYdnhW/1fbyomJ9vzLwlpqte7tfWXvDX/zaJVW8MeLff3PGJ+rUt387Od/mMa5FYVer7dpIl6USxqrdJdBWJVUxXqfcyQL7q696s8XuPSYOVGXtcPJimNlfRHx5fH/KMr21sXuYpOZborLQfMS848J9PW/5Yf0d2yO7QD2JpypE4SazqEk29nfp7z+8ON+g5b+35Yyv3fDuZmQUAtG/4OVg+ILGKNCmLxaoV0s/EJG20Mn2sYmYybD9gzAgFwGA4fayaW5OLEMKzIYQnQwhPlldILwkhTsT9miqt+Bt/IcTJUawSolgUq8QycNYbrFtmdhUAjv6+fcbXF2IZka6EKBZpSohikabEueKsN1hfAPCxo39/DMDvn/H1hVhGpCshikWaEqJYpClxrjhNm/bPAfg/AN5nZtfN7OMA/jmAnzGz1wD8zNHPQogpka6EKBZpSohikaaEOEWTixDCRyP/9ZdPeq58VELvrYkEWJ7Txt9wTxIfy0NfVt3lJw2kl4YkHzJv8M9PGnQAwGt9n9/5SMW/yX6z9DY9553MJxS+NPLmFS/1/Nu5v3zz3fSco33y1m6SO1vd4yYXtbd2XZkNiFFEyXdeqPHEwWqTvLY7+GToQM4JAHmFGGo0ffKiETOFvS7JMAaQ9WZnclGUrkKaYLw10b5Y3m3m+9DIFCiNSeJwP6IpkrifEqGNyrxSWz3/XP6rPT//O4lPPc5rb9FzJqRR3xt7nb7cf9SVvXjPlwFAd8/PoSrpk8oBb2dt2ycul/dIOnVGjFsqXBPlrtdaktVcWbBIIjnRFJsPRkxPMnJtAMgiBitnQaGxalxC/4Zfn/jBvshIP5RHJ4hVZMiGzKQpYtQwSL0GWay6XNlxZasJ19UO/Bz+49FlV8Zi1R/c+mF6zsG+n68N1k/MEAZA7WbXlSVdr6uQEK1WI7Gq5euE4HPy4rGKFpNz+jr1uuTaAPLB8fEMzPDhFChSUyFNMLoz0Y+nEKsq+7FYRUzSyKFpld8X7A69mc03SaxaLR24shKmj1VvjqfT1AvbvgwAenu+nrUB0VQ3cv+37e/1SixWheljVYXoPMmIkV1ygljF5g6ZN6MuufcEAGbwFWFuTS6EEEIIIYQQYtHQBksIIYQQQgghCkIbLCGEEEIIIYQoiHfcYJnZJ8yMv0VTCPFASFdCFIs0JUSxSFNCPDjTfIN1BcDzZva7ZvaUWeTV0kKIkyBdCVEs0pQQxSJNCfGAvKMdWgjhH5rZPwLwswD+GoB/bWa/C+DTIYQ3TruCAJCMgcaN4y4jzMUFACo9X8bcecZtbyUytYsPgDJxAssjzkz7Pe+E8vwd7+Ty6p53a/raxnfpOZvJyJV948C7yLyy7d1q7u15ZyMAwMjvt5mD4sEV3s7qbseVVbb9OfOG7+i8zjufuTgxp6wYzDEmIW5T+U3vTFMiBogAQEyFTsysdWUpULszMY7cHAgJcZJkYxDItDDy2aP/8SXk+mPweXGn7Ofa/00fd2VvtDZd2Q+1uYtmo+Q1tTX0jnDXu2uu7PYud44LxHGStZMYIwEA0qbv1FKfdDRxWwzEPe7wBGQCE50YN2Cj84Gth0aFenpPpc9aUwBgY6B+c2J8Io5nbH1h621KzExj8Y9dq3JA1tCIa9fOvr/YV8zr6vX9i67s+dUb9JxNoqtv9S64slfv+fi3vRNxZBySWEWa1L/A51vtno+BFaJL1k95nd82hTK5FhunyHygbndkjtgWcQEdT3fzYidwQAPmR1O128fHgfUVcHivOAlbhnJiEBdb71h5Zd+fdJzwebFV93P4D3OmKR+rvtLZoudkserGYNWVfXtvw5XdjmgqsLhCGLX5HBq3ybwc+GARyB49RO6dozFsguh8IGNXIs6IuOevb/l0/fGDmKr2IYQA4ObRnxTAOoD/aGb/4qFrIMQ5RboSolikKSGKRZoS4sF4x2+wzOxv4fCt21sA/j2AvxdCGJtZAuA1AH//dKsoxPIhXQlRLNKUEMUiTQnx4EzzxtRNAH81hPCd+wtDCLmZ/fzpVEuIpUe6EqJYpCkhikWaEuIBmSYH69d+wP+9Umx1hDgfSFdCFIs0JUSxSFNCPDjTfIM1cywHKvvHy0ojninKkt0OrvqktrTlP18iidpAJFFuj1w75Slt45FPKryTtFzZzY6/0DfDNXpOVH1Dy3WfUJgOyBATM4sYWcP3U+9KJFE2+Ez91m1iaFGePtE2rftjs9p0BgkAN1mo7pBzEpOBMjFMAXgi7aJhmddUzJCC9W3qpy9yMqylSF/RaxH9xTSZHvgB26v6sp3mmit7tXyVV6ri57qVfeMD03lEU0aS8XNiSDHq8Hb2Lnn9BKIfS5lLBT8n009WnV6TbOxKA3JcdgKdRsoXDaarWKxiZgeDTbbekVjFErXBDUioUcI4EqsGXthbZW8IcavtB+wVmz5WlWo+1mUniFVGFpus7vupf5H3k2XeeKrZJtdnfjCR8JnV/H+w+GUhcu9C9FLZZ/PBf7ZM9Ad4rcZNh+YXdv8Xi8FsHRmt+DJmEnMSUx+23pXIWg8A4743jrlX8bFqi8Sqb5Ye4ZUimkqIS0s+IkYNMU0RA5ScxMThOtfUwcg7h+REE9RMKxJ+svp0morB5ju7r7OxPye77wfi84ShFw0LIYQQQgghREFogyWEEEIIIYQQBaENlhBCCCGEEEIUhDZYQgghhBBCCFEQC2FyEQzIJ3K9Kwf8WPrWbXJsRpNPp69TuecPTiJvSWeJquU+qVPNJ7SXhpGEWPLa+t5ln2RYIi94T1s8ozwjJhssyTH1Oc8AePJjiSRZJpk/57jB+25y3AGe0Gt5ZPBIon/mc05RjswnBkuaXUQmE31LMZMLMl40yZ4MQUIS7AGepEzneiyflb3gnkzrUGa/Q+K/V2LmK2ysc5JkzhLsAa4fIl1qGgIAA5L4HhK/bJfG5DqRX5/lpemShFk9AfDuI6ekieiRS+exay0a5vuNJckDPFaxBOzcL+snSsCuHBD9jvhA1HbJGk40nFX9HEzIHDyslC8aXJjOvCFt8nNmbRKryN0MM7MCgOEGS2onsYqYx6TEJAYAArk+1UDsPoOclsY/cu8QYzJWx9aEuWeib2JzjemizGLVtOtVpLw8IOtt5B6idpeckxpK+PkXM/9ha/holRzHNEWMywAew6imIvd/A6KpYNPFqpjxGYsLtE9iIY1pipyzNJr+nEyTMRZVbkIIIYQQQggxd8zkGywz+zaAfQAZgDSE8OQs6iHEMiFdCVEs0pQQxSJNifPCLB8R/EshBPagjxDiwZGuhCgWaUqIYpGmxNKjRwSFEEIIIYQQoiBm9Q1WAPDfzSwA+LchhGcnDzCzZwA8AwDltXX0rxxPjAuRRO3qHkngI0UsyTdhiW4AKl2WJOyPS7o8I5ElRFZ3fOYzS96r3erSc+Ykybj3uM+UH6z5jL7uo3xf3WcmF1MmPgI8SZK9iZsZJDDjicNjyXXYsZHEYfZ5diwzuRh3+DljidNzwA/U1TFNra779kWSOst9YghD5irTRMykpUKS+ZlOSiOuqVLfl5d7fv5aRt54P46YvNS8VgYXfZbwYN3P6f5F3nnDdV/GkuHHkTnFE/+ZmQxLMKan5AYJJ0gcnjZRnhkusPYAQBZJvJ4DThyrBpemi1WVfd9mZrSUDB8yVhGTilJEA+WBL6/sEV0RU6HKXSJqAIHFqkd9pnx/nRg3XeV91ycayGu+7iGSPE91RYyWWKxipiMAN0OgBieRqR4zWfB18mUx4wEXq+bjV+on09TqOobrE5pK+Lgykxi2tnGTJV5ZZmjGYxUf2DKJVZUDMjGYmdQBnxR53WtqcNlP6v4GM0OLxKpNYtxBTJrGEUMidl/GYlXCYlXknEw/LK6cBHbvwuZDVuefjxnvMGa1wfqJEMLbZnYJwBfN7BshhC/ff8CR6J4FgNpjj81t9BVijviBurpfU/Vr0pQQU3CiWFV/VLoS4h04maYUq8SCMpPfZ4QQ3j76+zaA3wPwoVnUQ4hlQroSolikKSGKRZoS54Uz32CZWcvMOt//N4CfBfDyWddDiGVCuhKiWKQpIYpFmhLniVk8IngZwO/Z4QtgywB+J4TwX2dQDyGWCelKiGKRpoQoFmlKnBvOfIMVQngTwAfO+rpCLDPSlRDFIk0JUSzSlDhPzPI9WNNTDsguH7d46Ru3o8pqEeuryVMS1zjmggZwdxnmIlMjzoAAULvd9+fc2vUH5sQFacitbZLUX6s9uuKPe3zFX6bMh33c9uVZk9SpwW1ckpavU79R8ccNyZOp3NSKusswZxnmtBU71jJ/bNqe7toAkLUjlV0gQgkYreUTZbwPmWMTddFkZVwSSMbEmYm4LVV3uItSZdsL2O7tubKQkrkaIs6ENb+mlPreBtBy79aZV/hkyeq+78Zt4szZ4nVKmYvTKnFmIvPf8shaSC5FnZUiY8f0wz5vZJmJObDNsTPniQjlgPHm8TkbzK+BAJBXyPgwh1MfPqIugkyXzBmwdi8Sq+54G7Zk2+sKJP6EMdcqcxxsDS776zzubVvzSixW+RiSEXevvMF1ZU0Sq0j8Swb+OmyuA9xd09Lp1s7YsdQxjcyRnE8xpBOxaloH0HkilIHx+mQ7eENy4ho5rWMgi0kAUGKxit3/3ePzv3qHxKqdfVcWRuTzeeRei9zDtfqb/jqZv7GJxiriosnW5azF65RVvTDSjr+WsVhFYkoM5oJrEQfOhMUqdkvAbkkLiFULKDchhBBCCCGEmE+0wRJCCCGEEEKIgtAGSwghhBBCCCEKQhssIYQQQgghhCiIhTC5sCSgXD2emZbWeKZpueeT6qq7JElx6MsOHokkTpJk5Jzk/da3I0mSxNAiu33HH5j57Dur8ky7MPKZslb29R+3fFlKkhkBoEp8N8Yk+y9d4xmFnbbPxm5d8Cd9+6Y3DggnyHHPxiTBucXbVN7186FExj4jYzxa53OsetEngi8cSUBoHp9v2Zgnv1a6zFTBH8eSgbPqCZJXSZJ3NTIxbN+PQX5vx5cRkxgr84zwpOHntW2uujJm+hFLHmcJ1kaaNDkW36fS8jovl/28HPT8OhGITgAALKGYJdiTMgAokcR/1k6WjJ8SIwIAyFcjjhqLRhJQmjAByms8zCYj3781EquYocVgk4/NkCT5B/PjVd+mH0dy1yffZ3e2/DnHfrwsYkgBYnJBqoS07gvzyPpR8dWkGgzrfF61V3ysql/wx25tEeONmHlMIOVjX5ZFPl/eY7HKH5eBGOesRox7LkycgKwdc08SECZMSbIhX8Oru75vKl2iKbJepZF7CGpeQpbreixW7XmTi2zLC5De05V4TDZiyIR8wxexe9eImVVp4MuyOjkwYnLW6PjJWi77Y7t7DV+nNBKryHTNyLHRWHUwXaxiPkTMjAoAwkbEpYagb7CEEEIIIYQQoiC0wRJCCCGEEEKIgtAGSwghhBBCCCEKQhssIYQQQgghhCiIhTC5KCU5VjvHk1K3t1j23eFbvydhb14fdXxS3OBCxKSCJJqyN4YPDnjiZf3bkdesTxBSkjgcMblILviExsHllivrPuKTJEc+bx8AkFd9+1lSer3Jk/w22z6Z82K966+f+Trt7DXpOdMRSfIc+d8LhCSSkFgiCdYkSZK9tTtr84TgZnXxE/KtFFBpHx/HdMg1lZG+KZEpHYgmxn5KAgASkvwdEj8upRGf/5V7/sTW9fPPMj+GVuV6tNUVVzZa9wm5gzViJkPWEwBIG8T4o+7rVG7wObXa9pnH7ZpfkLp1n/R8MOB9Nxr5RTLr+TI2HgCQkcxjZqTAtJe3I2YeTW6cs2gkSUCrdXzM9u/yccinCwsYt/3cGm7w9S4ZEl2RpPZKn1+8dt3PIzNyzpwYMpVI4j2AZN0bRQwuev0eXPFr/TASqzKmq6afl1ViXAMAG01vcrFR9+sHY7fr1wSAx6o8YsbAyCu+TSUynnmNtD2iq2btePuTSJycZywJqE/EquEBv3VlmmLzPyXHxe6LmIECO2d5GIlVt/18sR1mSnSC+781X9nhBX8P1b9AYpUPcwD4vR6LVZWIplZbXlOrNR+/7lZ8O/d7/N5jzGLVARmPmL8O0YoRM5qchLpYrKrWp7//0zdYQgghhBBCCFEQ2mAJIYQQQgghREFogyWEEEIIIYQQBaENlhBCCCGEEEIUhDZYQgghhBBCCFEQC+EiSOlwJ4/e1encyRJihBfK3DVuRAxOmOPKaIU7iWXVK65s7RttV1a+tePrVOFDlF72LjL9C/7Y0Zr/7GiVtzMQwz4Q16GUuPABQE7cWUa5P+kj7T1XVi1xx5b9gXem6pI6BeKCBgBZi7SVDBNzoEKV91Oaso5aLMwCqrXjGkrbXFMj4vgH4hpn5ONpK+JaRU6ZNonbUzPmFrXmylodP1dKd4kzWJmP32jDuzD1LnsXp+EGmecrvJ1snQjErdMSPteqZd+pjbJ3caJlFe7q1iVuVwfEAW484H0fKn7ssxqpP3NFq0VcBJfAmRM41FVpciwjsWpwkcULEqvIx5mzJwCkZB1juhq3Iw6RlYuubHXN66J8e9eVxWLV+KJ3Eexf9DZuwzX/2dFaxB2WzC0QWec5j8lm07npnSRWHRBd7ZNuZo6dAJCTsRuTe5ecOLsZ0x+AMBGTF89D8HCsKhPOc8NIrGJr87SxahyLVWQMU2J6nLZ4XMkqF1xZp+NvKkt3veMySvyc401//9i7RGLVOolVq7ydGXHcozqLUCExjGnlUsu3s07iHMBjVTfxfReLVTm5V8yrZI6QdiYRt8BymeufoW+whBBCCCGEEKIgZrLBMrOnzOybZva6mX1yFnUQYtmQroQoFmlKiGKRpsR54cw3WGZWAvDbAH4OwPsBfNTM3n/W9RBimZCuhCgWaUqIYpGmxHliFt9gfQjA6yGEN0MIIwCfB/D0DOohxDIhXQlRLNKUEMUiTYlzwyxMLq4B+N59P18H8OcnDzKzZwA8c/Tj8Kt/5bdePoO6nSWbALYe+NOvFVeRgth842HaM5+cZIweP82KTME76mpSU6/80q9LU3POG8vXpqXSFOB19dIv/OYy6erh59+rpOwPHuqMD8t5jlULqamvP/0by6QpYAnX9XOsKSCiq1lssJitj7PwCCE8C+BZADCzF0IIT552xc6SZWvTsrUHWLg2vaOupKnFY9natGDtOfexatnaA6hNM+bcawpYvjYtW3uAYto0i0cErwN47L6fHwXw9gzqIcQyIV0JUSzSlBDFIk2Jc8MsNljPA3ivmT1hZlUAvwzgCzOohxDLhHQlRLFIU0IUizQlzg1n/ohgCCE1s08A+G84fDXgZ0IIX3+Hjz17+jU7c5atTcvWHmCB2vQAulqYtp0AtWn+WZj2KFYBWL72AGrTzJCm/oRla9OytQcooE0WwiK+21sIIYQQQggh5o+ZvGhYCCGEEEIIIZYRbbCEEEIIIYQQoiDmeoNlZk+Z2TfN7HUz++Ss6/MgmNlnzOy2mb18X9mGmX3RzF47+nt9lnU8KWb2mJn9TzN7xcy+bmZ/+6h8YdtlZnUz+4qZvXTUpl8/Kl/YNsWQruYPaWqxkabmD2lqsZGm5pNl09VpampuN1hmVgLw2wB+DsD7AXzUzN4/21o9EM8BeGqi7JMAvhRCeC+ALx39vEikAH41hPCjAH4cwN88GptFbtcQwE+FED4A4IMAnjKzH8dit8khXc0t0tSCIk3NLdLUgiJNzTXLpqtT09TcbrAAfAjA6yGEN0MIIwCfB/D0jOt0YkIIXwZwd6L4aQCfPfr3ZwH84lnW6WEJIdwIIXz16N/7AF7B4RvaF7Zd4ZDu0Y+Voz8BC9ymCNLVHCJNLTTS1BwiTS000tScsmy6Ok1NzfMG6xqA79338/WjsmXgcgjhBnA4WQFcmnF9HhgzexeAPwPgD7Hg7TKzkpm9COA2gC+GEBa+TQTpas6RphYOaWrOkaYWDmlqAVgWXZ2WpuZ5g2WkTJ7yc4SZtQH8JwB/J4SwN+v6PCwhhCyE8EEcvl3+Q2b2YzOu0mkgXc0x0tRCIk3NMdLUQiJNzTnLpKvT0tQ8b7CuA3jsvp8fBfD2jOpSNLfM7CoAHP19e8b1OTFmVsGhuP5DCOE/HxUvfLsAIISwA+B/4fDZ6aVo031IV3OKNLWwSFNzijS1sEhTc8yy6qpoTc3zBut5AO81syfMrArglwF8YcZ1KoovAPjY0b8/BuD3Z1iXE2NmBuDTAF4JIXzqvv9a2HaZ2UUzWzv6dwPATwP4Bha4TRGkqzlEmlpopKk5RJpaaKSpOWXZdHWqmgohzO0fAB8B8CqANwD8g1nX5wHb8DkANwCMcfhbmY8DuIBDV5LXjv7emHU9T9imv4DDr+v/CMCLR38+ssjtAvCnAXztqE0vA/i1o/KFbdMPaKt0NWd/pKnZ1/ch2ypNzdkfaWr29X3ItkpTc/hn2XR1mpqyoxMJIYQQQgghhHhI5vkRQSGEEEIIIYRYKLTBEkIIIYQQQoiC0AZLCCGEEEIIIQpCGywhhBBCCCGEKAhtsIQQQgghhBCiILTBEkIIIYQQQoiC0AZLCCGEEEIIIQpCG6xzhJn9OTP7IzOrm1nLzL5uZj8263oJsahIU0IUizQlRPFIV2ePXjR8zjCzfwqgDqAB4HoI4Z/NuEpCLDTSlBDFIk0JUTzS1dmiDdY5w8yqAJ4HMADw4RBCNuMqCbHQSFNCFIs0JUTxSFdnix4RPH9sAGgD6ODwNxlCiIdDmhKiWKQpIYpHujpD9A3WOcPMvgDg8wCeAHA1hPCJGVdJiIVGmhKiWKQpIYpHujpbyrOugDg7zOxXAKQhhN8xsxKA/21mPxVC+B+zrpsQi4g0JUSxSFNCFI90dfboGywhhBBCCCGEKAjlYAkhhBBCCCFEQWiDJYQQQgghhBAFoQ2WEEIIIYQQQhSENlhCCCGEEEIIURDaYAkhhBBCCCFEQWiDJYQQQgghhBAFoQ2WEEIIIYQQQhTE/wfUj5VomrITlgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scene = Scene.at('./data', 0)\n",
    "plot(scene.read('batched_noise_data'), show_color_bar=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "\u001B[94m(0.649, 0.992, 0.729, 0.875)\u001B[0m along \u001B[92mbatchᵇ\u001B[0m \u001B[93mfloat64\u001B[0m"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene.properties['smoothness']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Scenes themselves can also be batched.\n",
    "In that case, a single `Scene` object represents a batch of actual directories.\n",
    "When writing data to the batched scene, the data are sliced along the dimensions of the scene."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "\u001B[94m(./data\\sim_000001, ./data\\sim_000002, ./data\\sim_000003, ./data\\sim_000004)\u001B[0m along \u001B[92mbatchᵇ\u001B[0m"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene_batch = Scene.create('./data', batch(batch=4), name='sim')\n",
    "scene_batch"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "scene_batch.write(batched_noise_data=batched_data, frame=0)\n",
    "scene_batch.put_properties(smoothness=batched_smoothness)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, let's look at the contents of a single scene."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "0.6488508582115173"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene = Scene.at('./data', 1)\n",
    "scene.properties['smoothness']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 2 Axes>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(scene.read('batched_noise_data'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "If the scene has batch dimensions that are not present on the data, the data are tiled automatically, i.e. stored redundantly across the scenes."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}